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In this report, you will find analysis on meeting subject lines. This will allow you to refine meeting exclusion rules and identify topics that drive collaboration patterns.
---
params:
data: data
stopwords: stopwords
set_title: report_title
keep: keep
seed: seed
title: "`r params$set_title`"
---
```{r echo=FALSE, message=FALSE, warning=FALSE}
library(wpa)
## Get user data
data <- params$data
stopwords <- params$stopwords
keep <- params$keep
seed <- params$seed
```
Introduction
===============================================
Column {data-width=40%}
-------------------------------------
### Introduction
In this report, you will find analysis on **meeting subject lines**. This will allow you to refine meeting exclusion rules and identify topics that drive collaboration patterns.
Column {data-width=60%}
-------------------------------------
### Word cloud
```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>% tm_wordcloud(stopwords = stopwords, keep = keep)
```
Word Frequency
===============================================
Column {data-width=50%}
-------------------------------------
### Word Frequency - Plot
```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>% tm_freq(token = "words", stopwords = stopwords, keep = keep)
```
Column {data-width=50%}
-------------------------------------
### Word Frequency - Table
```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>%
tm_freq(
token = "words",
stopwords = stopwords,
keep = keep,
return = "table"
) %>%
create_dt()
```
Phrase Frequency
===============================================
Column {data-width=50%}
-------------------------------------
### Phrase Frequency - Plot
```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>% tm_freq(token = "ngrams", stopwords = stopwords, keep = keep)
```
Column {data-width=50%}
-------------------------------------
### Phrase Frequency - Table
```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>%
tm_freq(
token = "ngrams",
stopwords = stopwords,
keep = keep,
return = "table"
) %>%
create_dt()
```
Word co-occurrence
===============================================
Column {data-width=50%}
-------------------------------------
### Word co-occurrence - Plot
```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>% tm_cooc(stopwords = stopwords, seed = seed, return = "plot")
```
Column {data-width=50%}
-------------------------------------
### Word co-occurrence - Table
```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>%
tm_cooc(stopwords = stopwords, seed = seed, return = "table") %>%
create_dt()
```
Top terms
===============================================
Column {data-width=50%}
-------------------------------------
### Top terms - Days
```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>% subject_scan(mode = "days", stopwords = stopwords)
```
Column {data-width=50%}
-------------------------------------
### Top terms - Hours
```{r echo=FALSE, message=FALSE, warning=FALSE}
data %>% subject_scan(mode = "hours", stopwords = stopwords)
```